Configuration Path Control

Reinforcement learning methods often produce brittle policies — policies that perform well during training, but generalize poorly beyond their direct training experience, thus becoming unstable under small disturbances. To address this issue, we propose a method for stabilizing a control policy in t...

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Veröffentlicht in:	International journal of control, automation, and systems automation, and systems, 2023, Vol.21 (1), p.306-317
1. Verfasser:	Pankov, Sergey
Format:	Artikel
Sprache:	eng
Schlagworte:	Configurations Control Engineering Mechatronics Perturbation Policies Production methods Regular Papers Robotics Training
Online-Zugang:	Volltext
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